In many problems, the measured variables (e.g., image pixels) are just mathematical functions of the latent causal variables (e.g., the underlying concepts or objects). For the purpose of making predictions in changing environments or making proper changes to the system, it is helpful to recover the latent causal variables $Z_i$ and their causal relations represented by graph $\mathcal{G}_Z$. This problem has recently been known as causal representation learning. This paper is concerned with a general, completely nonparametric setting of causal representation learning from multiple distributions (arising from heterogeneous data or nonstationary time series), without assuming hard interventions behind distribution changes. We aim to develop general solutions in this fundamental case; as a by product, this helps see the unique benefit offered by other assumptions such as parametric causal models or hard interventions. We show that under the sparsity constraint on the recovered graph over the latent variables and suitable sufficient change conditions on the causal influences, interestingly, one can recover the moralized graph of the underlying directed acyclic graph, and the recovered latent variables and their relations are related to the underlying causal model in a specific, nontrivial way. In some cases, most latent variables can even be recovered up to component-wise transformations. Experimental results verify our theoretical claims.
A Sparsity Principle for Partially Observable Causal Representation Learning
University of Amsterdam（阿姆斯特丹大学）；Institute of Science and Technology Austria（奥地利科技学院）；Max Planck Institute for Intelligent Systems（马克斯·普朗克智能系统研究所）；Samsung - SAIT AI Lab, Montreal（三星SAIT AI实验室（蒙特利尔））；Mila, Université de Montréal（蒙特利尔大学Mila研究所）；ServiceNow Research（ServiceNow研究院）；Seminar for Statistics, ETH Zürich（苏黎世联邦理工学院统计研究所）
Causal Mechanism Estimation in Multi-Sensor Systems Across Multiple Domains
Bosch Center for Artificial Intelligence（博世人工智能中心）；Institute of Industrial Manufacturing and Management IFF, University of Stuttgart（工业制造与管理研究所）；Fraunhofer Institute for Manufacturing Engineering and Automation IPA（弗劳恩霍夫制造工程与自动化研究所）
How Useful is Causal Invariance for Domain Adaptation in Finite-Sample Settings?
Department of Computer Science, ETH Zurich（苏黎世联邦理工学院计算机科学系）；Causal Artificial Intelligence Lab, Columbia University（哥伦比亚大学因果人工智能实验室）；Department of Statistics, Columbia University（哥伦比亚大学统计系）
Scalable Contrastive Causal Discovery under Unknown Soft Interventions
Department of Systems Biology, Columbia University Irving Institute for Cancer Dynamics, Columbia University（系统生物学系，哥伦比亚大学伊万尼癌症动力学研究所，哥伦比亚大学）；Irving Institute for Cancer Dynamics, Columbia University（伊万尼癌症动力学研究所，哥伦比亚大学）；Department of Computer Science, Columbia University（计算机科学系，哥伦比亚大学）；Department of Biomedical Engineering, Columbia University（生物医学工程系，哥伦比亚大学）；Data Science Institute, Columbia University（数据科学研究所，哥伦比亚大学）
Korteweg-De Vries Institute, University of Amsterdam（阿姆斯特丹大学科特韦赫-德弗里斯研究所）；MIT-IBM Watson AI Lab, IBM Research（IBM研究院麻省理工-IBM沃森人工智能实验室）；Institute for Computing and Information Sciences, Radboud University Nijmegen（拉德堡德奈梅亨大学计算与信息科学研究所）
Causal Multi-Label Feature Selection in Federated Setting
School of Computer Science and Information Engineering, Hefei University of Technology（合肥工业大学计算机与信息工程学院）；School of Information and Artificial Intelligence, Anhui Agricultural University（安徽农业大学信息与人工智能学院）
Causal Effect Identification in LiNGAM Models with Latent Confounders
Technical University of Munich（慕尼黑工业大学）；Ecole Polytechnique Fédérale de Lausanne（洛桑联邦理工学院）；Leiden University（莱顿大学）；Munich Center for Machine Learning（慕尼黑机器学习中心）
Learning Causal Structures Using Regression Invariance
University of Illinois at Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；Coordinated Science Laboratory, University of Illinois at Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校协调科学实验室）；Carnegie Mellon University（卡内基梅隆大学）
Horizontal and Vertical Federated Causal Structure Learning via Higher-order Cumulants
School of Computer Science, Guangdong University of Technology（广东技术大学计算机科学学院）；Peng Cheng Laboratory, Shenzhen, China（深圳鹏城实验室）；College of Science, Shantou University（汕头大学理学院）；Department of Philosophy, Carnegie Mellon University（卡内基梅隆大学哲学系）；Mohamed bin Zayed University of Artificial Intelligence（穆罕默德·本·扎耶德人工智能大学）
Do Finetti: On Causal Effects for Exchangeable Data
Toyota Research Institute（丰田研究所）；Oregon State University（俄勒冈州立大学）；Max Planck Institute for Intelligent Systems（马克斯·普朗克智能系统研究所）；University of Cambridge（剑桥大学）
M-CaStLe: Uncovering Local Causal Structures in Multivariate Space-Time Gridded Data
Scientific Machine Learning, Center for Computing Research（科学机器学习，计算研究中心）；Sandia National Laboratories（沙伊达国家实验室）；Zicklin School of Business Baruch College, CUNY（巴鲁克学院Zicklin商学院，纽约大学）；Department of Computer Science University of New Mexico（新墨西哥大学计算机科学系）；Department of Atmospheric & Oceanic Science University of Maryland（马里兰大学大气与海洋科学系）；Santa Fe Institute（圣菲研究所）
Proximal Causal Learning with Kernels: Two-Stage Estimation and Moment Restriction
University College London（伦敦大学学院）；University of Oxford（牛津大学）；The Alan Turing Institute（艾伦·图灵研究所）；ENSAE/CREST（法国国家统计与经济管理学院/经济与统计研究中心）；Max Planck Institute for Intelligent Systems（马克斯·普朗克智能系统研究所）
Causality Is Key to Understand and Balance Multiple Goals in Trustworthy ML and Foundation Models
CISPA Helmholtz Center for Information Security（CISPA海德堡信息安全中心）；Max Planck Institute for Intelligent Systems, Tübingen（马克斯·普朗克智能系统研究所（图宾根））；Google Research（谷歌研究）；ETH Zürich（苏黎世联邦理工学院）；University of Toronto（多伦多大学）
